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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2407.20570 |
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| _version_ | 1866929441756676096 |
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| author | Gao, Lin Lu, Jing Shao, Zekai Lin, Ziyue Yue, Shengbin Ieong, Chiokit Sun, Yi Zauner, Rory James Wei, Zhongyu Chen, Siming |
| author_facet | Gao, Lin Lu, Jing Shao, Zekai Lin, Ziyue Yue, Shengbin Ieong, Chiokit Sun, Yi Zauner, Rory James Wei, Zhongyu Chen, Siming |
| contents | Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20570 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education Gao, Lin Lu, Jing Shao, Zekai Lin, Ziyue Yue, Shengbin Ieong, Chiokit Sun, Yi Zauner, Rory James Wei, Zhongyu Chen, Siming Human-Computer Interaction Large Language Models (LLMs) have shown great potential in intelligent visualization systems, especially for domain-specific applications. Integrating LLMs into visualization systems presents challenges, and we categorize these challenges into three alignments: domain problems with LLMs, visualization with LLMs, and interaction with LLMs. To achieve these alignments, we propose a framework and outline a workflow to guide the application of fine-tuned LLMs to enhance visual interactions for domain-specific tasks. These alignment challenges are critical in education because of the need for an intelligent visualization system to support beginners' self-regulated learning. Therefore, we apply the framework to education and introduce Tailor-Mind, an interactive visualization system designed to facilitate self-regulated learning for artificial intelligence beginners. Drawing on insights from a preliminary study, we identify self-regulated learning tasks and fine-tuning objectives to guide visualization design and tuning data construction. Our focus on aligning visualization with fine-tuned LLM makes Tailor-Mind more like a personalized tutor. Tailor-Mind also supports interactive recommendations to help beginners better achieve their learning goals. Model performance evaluations and user studies confirm that Tailor-Mind improves the self-regulated learning experience, effectively validating the proposed framework. |
| title | Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in Education |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.20570 |